sentiment analysis Archives - BotCore Enterprise Chatbot Fri, 15 Mar 2024 09:55:55 +0000 en-US hourly 1 https://wordpress.org/?v=6.6.1 https://botcore.ai/wp-content/uploads/2020/02/cropped-favicon-32x32-1-70x70.png sentiment analysis Archives - BotCore 32 32 Using Sentiment Analysis to Improve the Conversational User Experience https://botcore.ai/blog/sentiment-analysis-to-improve-the-conversational-user-experience/ Mon, 12 Jul 2021 11:47:00 +0000 https://botcore.ai/?p=7936 Using Sentiment Analysis to Improve the Conversational User Experience Analysis firm Juniper Research predicts, “By 2022, chatbots could help trim business costs by more than $8 billion per year.” The same study anticipates a significant surge in automated customer service programs as companies move to embrace artificial intelligence (AI) and predicts that in industries, which […]

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Using Sentiment Analysis to Improve the Conversational User Experience

Analysis firm Juniper Research predicts, “By 2022, chatbots could help trim business costs by more than $8 billion per year.”

The same study anticipates a significant surge in automated customer service programs as companies move to embrace artificial intelligence (AI) and predicts that in industries, which manage a large volume of human interaction, 75-90% of queries will be dealt with by chatbots, resulting in cost savings of up to $0.70 per interaction.

Indeed, AI has revolutionized how businesses interact with their customers. However, enterprises looking to replace, or at least augment, their traditional customer interactions with AI don’t want to lose the human touch.

Humans are emotional beings, and the ability to gauge sentiments and respond with empathy is ingrained in our language instincts. No wonder, during interactions with service chatbots, customers expect the bots to understand their tonality, mood, and sentiments and respond accordingly.

That is where sentiment analysis comes into the picture. This blog gives an insight into what sentiment analysis is and how it is used in chatbots to enhance the overall customer experience.

What is sentiment analysis?

Sentiment analysis is a sub-category of natural language processing (NLP) and machine learning (ML) that extracts emotions, thoughts, and opinions from audio or textual data.

In short, sentiment analysis enables bots to comprehend the user’s mood and sentiments by analyzing utterances for words and phrases that indicate a particular emotion. The bot can then model its response suitably to provide excellent customer service. Using machine learning tools to train the bot with words/phrases that correlate to different moods, sentiment analysis teaches the bot to detect emotions without any human intervention.

For example, A customer writes, “I am annoyed with your team. I have been sent items that I didn’t order.”

The bot responds by saying, “We are incredibly sorry for the mistake. The correct package will be delivered today by 16:30 hours.

Here, sentiment analysis renders emotional intelligence to the bot and allows it to understand that the phrase annoyed is an expression of the customer’s disgust and anger at the callousness of the team.

Hence, sentiment analysis helps bots conduct thoughtful, engaging, and meaningful conversations with customers.

Here’s how the process flows –

  • The bot starts with understanding the polarity of the conversation. Polarity indicates whether the sentiment displayed in the conversation is positive, negative, or neutral. The bot can detect emotions, such as joy, anger, sadness, fear, disgust, curiosity, and many more.
  • Then the AI and NLP technologies measure the magnitude or intensity of the emotion and assign a numerical score to each of the sentiments.
  • The final score helps the bot to decide a further course of action. For example, for an utterance (voice/textual) that has a high positive score (joy/happiness), the bot can leverage the opportunity to upsell. On the other hand, for a conversation with a high negative score (sadness/anger), the bot may escalate the call to a live agent for effective remediation of the issue.

Sentiment analysis is a sub-category of natural language processing (NLP) and machine learning (ML) that extracts emotions, thoughts, and opinions from audio or textual data.

In short, sentiment analysis enables bots to comprehend the user’s mood and sentiments by analyzing utterances for words and phrases that indicate a particular emotion. The bot can then model its response suitably to provide excellent customer service. Using machine learning tools to train the bot with words/phrases that correlate to different moods, sentiment analysis teaches the bot to detect emotions without any human intervention.

For example, A customer writes, “I am annoyed with your team. I have been sent items that I didn’t order.”

The bot responds by saying, “We are incredibly sorry for the mistake. The correct package will be delivered today by 16:30 hours.

Here, sentiment analysis renders emotional intelligence to the bot and allows it to understand that the phrase annoyed is an expression of the customer’s disgust and anger at the callousness of the team.

Hence, sentiment analysis helps bots conduct thoughtful, engaging, and meaningful conversations with customers.

Here’s how the process flows –

  • The bot starts with understanding the polarity of the conversation. Polarity indicates whether the sentiment displayed in the conversation is positive, negative, or neutral. The bot can detect emotions, such as joy, anger, sadness, fear, disgust, curiosity, and many more.
  • Then the AI and NLP technologies measure the magnitude or intensity of the emotion and assign a numerical score to each of the sentiments.
  • The final score helps the bot to decide a further course of action. For example, for an utterance (voice/textual) that has a high positive score (joy/happiness), the bot can leverage the opportunity to upsell. On the other hand, for a conversation with a high negative score (sadness/anger), the bot may escalate the call to a live agent for effective remediation of the issue.

How does sentiment analysis transform the overall conversational user experience?

1. Creates memorable customer interactions

One of the most significant benefits of sentiment analysis is its ability to decipher the customer’s mood and personalize responses to match the sentiment.

This helps the bot to create engaging customer interactions from the get-go, increasing chatbot adoption in the process.

2. Spots critical emotional triggers

Often standardized, scripted phrases, such as “Please wait” or “We will assist you shortly,” can evoke strong emotions and alter customer attitude.

Identifying such messages that brush customers the wrong way can help organizations change their service approach. Analyzing sentiment data enables bots to determine the kind of conversation flows that generate maximum customer satisfaction and improve the conversational user experience in the future.

3. Allows bots to handover angry customers to human agents

Sentiment analysis empowers bots to gauge customer emotion early on in the chat, allowing them to invoke appropriate escalation and route the angriest/most frustrated users to the suitable human agents for more efficient support.

Seamless escalation to a live agent leads to a better conversational user experience. It is especially advantageous during peak hours when the customer support staff finds it challenging to handle hundreds of customers and comprehend individual emotions. Using sentiment analysis ensures human agents handle only the most critical requests.

How does improving conversational user experience through sentiment analysis benefits the brand?

1. Helps gauge customer perception about your brand

Emotions play heavily in determining how a customer perceives your brand. Understanding how satisfied your customers are with your products and services will assist you in predicting the length and nature of their relationship with the brand.

Using sentiment analysis to comprehend how customers feel about your brand helps you communicate effectively and tailor experiences to improve brand perception.

2. Enables organizations to segment customers for more focused engagement

Sentiment analysis presents insights that enable you to segment your customer base by how happy or dissatisfied your customers are with your brand.

Using this information, organizations can prioritize support for the unhappy users and design strong branding and product strategies for the future while rewarding the most loyal customers at the same time.

3. Helps upsell and acquire new customers

With sentiment analysis, bots can identify delighted customers and generate upsell and cross-selling opportunities by recommending new/complementary products.

By analyzing the sentiments of new users, the bot can suggest the right products and retain their interest in your brand.

How can Acuvate help?

At Acuvate, we help clients build AI-enabled chatbots with our enterprise bot-building platform called BotCore.

BotCore’s chatbots come with a built-in capability to perform sentiment analysis. With minimalistic coding requirements and a graphical design interface, our bots can be built and deployed within a few weeks.

Interestingly, our chatbots can uncover a myriad of emotions, including anger, joy, curiosity, frustration, disappointment, fear, sadness, and positivity, and even identify and score multiple emotions (Example, joy and mild disappointment) in a single user utterance.

Moreover, our bots leverage the best of Microsoft’s machine learning, AI, and natural language processing technologies to understand user context, rank customer emotions at the individual and aggregate level (i.e., for the entire chat), identify vital emotional triggers, curate appropriate responses, and seamlessly hand over the conversation to a human agent when needed.

Additionally, our bots support multiple languages, such as French, Italian, English, German, etc.

To know more about BotCore and its sentiment analysis functionality, please feel free to schedule a personalized consultation with our experts.

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7 Advanced Chatbot Features To Consider in 2021 https://botcore.ai/blog/chatbot-features-2021/ Fri, 22 Jan 2021 05:23:00 +0000 https://botcore.ai/?p=7440 7 Advanced Chatbot Features To Consider in 2021 80% of businesses are expected to have some sort of chatbot automation by 2021. Business Insider The year 2020 has seen an unprecedented rise in the use of chatbots. Amidst the uncertainties caused by the pandemic and changing expectations about how brands should communicate with their customers, […]

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7 Advanced Chatbot Features To Consider in 2021

80% of businesses are expected to have some sort of chatbot automation by 2021.

Business Insider

The year 2020 has seen an unprecedented rise in the use of chatbots. Amidst the uncertainties caused by the pandemic and changing expectations about how brands should communicate with their customers, businesses have quickly adopted AI-powered bots to reduce the burden of their support staff and deliver easy, interactive, and more meaningful engagement to their customers.

No wonder chatbot technology has evolved to incorporate some powerful functionalities that will define the future of customer experience.

Research by Business Insider says, The global chatbot market is anticipated to reach $9.4 billion by 2024.

So, let’s have a look at the seven advanced chatbot features to consider in 2021.

Advanced Chatbot Features to consider in 2021

1. Augmented reality and chatbots

Augmented reality (AR) in chatbots opens a world of immersive, personalized, and engaging shopping experiences for customers.

Gartner defines augmented reality as the real-time use of information in the form of text, graphics, audio, and other enhancements integrated with real-world objects.

POND’S, a popular skincare brand,  launched a skin-diagnostic chatbot called SAL to assist consumers in dealing with common skincare problems across four areas – uneven skin tone, pimples, wrinkles, and spots. The bot leverages AI and AR to get an in-depth insight into the skin type and recommend suitable products. Customers need to simply upload a selfie, fill in a short survey, and the bot delivers a personalized skin diagnosis and product recommendations in less than a minute.

Such unique experiences generate buzz around the brand, boosting customer engagement and driving revenue in the process. Therefore, augmented reality will be a significant chatbot feature to consider in 2021, primarily for industries where buyers prefer a look-test or visual inspection of the product.  

2. Sentiment analysis and emotional intelligence

As the COVID-19 pandemic brought a wave of anxiety, confusion, and uncertainty, organizations recognized the increasing importance of responding to customers with empathy.

Sentiment analysis, therefore, becomes one of the most critical capabilities in a chatbot. Since tone and emotion significantly alter what a customer wants to convey, sentiment analysis allows bots to identify and understand the type and intensity of a customer’s sentiment, including anger, joy, fear, and frustration.

By deciphering words and sentence structures and extracting emotion, the bot can steer conversations, change the tone, or bring in a human agent for support. Hence, emotional intelligence will be a significant feature to look out for in bots in 2021.

3. Text-to-speech and speech-to-text

Another advanced feature that is fast-changing the world of bots is text-to-speech technology. This technology allows brands to develop a voice of their own by enabling bots to speak in a fluid, natural-sounding, human-like voice.

With text-to-speech bots, organizations can provide more engaging, accurate, and quick conversational IVR support.

So, the next time a customer wants to book a hotel room, he/she just needs to call up the contact center and say, “I want to book a hotel room,” instead of going through multiple IVR options. The bot will ask for other details in a human-like voice, book the hotel room or directly route the customer to the next available agent.

Additionally, bots may leverage speech-to-text technology to transcribe audio to text in different languages and variants accurately. In fact, research by Gartner suggests, “by 2023, 25% of customer interactions will be via voice.”

Many organizations have started leveraging Microsoft’s Azure Cognitive Services to convert text to life-like speech or convert spoken audio to text in more than 100 languages and variants.

4. Agent assistant capabilities

Despite chatbot technology growing at a rapid pace, in some situations, bots aren’t capable of handling customer needs entirely, and the conversation may require an agent handover. A customer may be angry or irritated, the issue may be complicated, or the conversation may involve high-value transactions with a customer at the risk of churning.

A few key chatbot capabilities that will ensure a smooth handover include –

  • Handing over chat transcripts, including details about context and sentiment analysis scores
  • Seamless integration with existing live agent software, including Salesforce, LiveChat, etc.
  • Translating queries for the human agents while routing the communication, in case of multilingual support
  • Agent observation, wherein agents merely monitor bot conversations instead of completely taking charge. In such cases, a bot privately takes agent authorization before recommending the solution to the customer.

5. Human-in-the-loop feedback system

Training, calibrating and explaining AI-enabled systems requires human-in-the-loop architecture.

– Gartner

Chatbots will come with a human-in-the-loop system to continually learn and become more intelligent. Small customer feedback, such as “click here if you are satisfied with the service,” can improve the machine learning algorithms and train the bot.

In addition to customer feedback, agent training plays a crucial role in enhancing bot performance. Contact center agents can classify outliers and exceptions, modify training data, and influence bot behavior.

6. Integration with RPA for end-to-end automation

Robotic Process Automation, or RPA, uses AI and machine learning to perform a variety of repeatable tasks, such as calculations, data entry, handling queries, etc.

RPA-chatbot integration is a powerful combination that can solve significant operational and workflow related issues for organizations. The automation capabilities of RPA combined with the cognitive abilities of chatbots can help enterprises automate processes end-to-end and reduce costs.

An RPA-enabled chatbot can integrate with multiple siloed and legacy back-end enterprise systems. RPA enables bots to retrieve information from such systems and handle more complex requests at scale.

Thereby, chatbots will not only handle queries and find information but also perform transactions on the user’s behalf, going from mere conversation to action.

7. Conversational maturity

Finally, the natural language processing capabilities that empower chatbots to understand the conversation context in multiple languages is an essential feature to consider.

Bots will be able to identify the intent of a query to provide a quick response and proactively seek information, ask clarifying questions, and confirm intent, even if the interaction isn’t linear.

Final Thoughts!

Chatbots have gained traction owing to their ability to provide real-time, on-demand resolutions that consumers are increasingly seeking out.

In light of their growing popularity, organizations must look out for specific features that enhance chatbot capabilities and enable them to deliver engaging, personalized, and more human-like conversations to users. 

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Building A Resilient Customer Service During Uncertain Times https://botcore.ai/blog/customer-service-during-uncertain-times/ Wed, 20 May 2020 14:09:00 +0000 https://botcore.ai/?p=5810 Building A Resilient Customer Service During Uncertain Times The onset of the Covid-19 pandemic has posed some unprecedented challenges to both public and private organizations. Measures implemented to control the spread of the virus and the impact caused by them have created confusion, uncertainty, and fear amongst consumers. The immediate need for clarity among customers […]

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Building A Resilient Customer Service During Uncertain Times

The onset of the Covid-19 pandemic has posed some unprecedented challenges to both public and private organizations. Measures implemented to control the spread of the virus and the impact caused by them have created confusion, uncertainty, and fear amongst consumers.

The immediate need for clarity among customers on a variety of issues, from loan payments to booking flights, from cancellations to insurance claims and mortgage payments, has led to a huge spike in the number of calls received by the contact centers. A majority of customers prefer call support as their initial channel of communication since it allows for greater flexibility and provides a human touch.

Customers want an opportunity to explain, reason or negotiate with contact center agents. Also, they generally prefer to solve urgent issues by calling for support rather than use other channels.

Many organizations across industries are ill-equipped to deal with such a massive increase in call volumes, leading to longer wait-times as the support staff serves other customers.

Adding to the turmoil is the need to comply with social distancing norms that have forced the support staff to shift to a work-from-home setup. Moreover, there is a shortage of service staff as workers fall sick and are unable to report to work.  

The pandemic has brought to the forefront the pressing need for businesses to be empathetic towards the needs of their customers while also implementing significant changes in their operational setups.

In times of crisis, a business’s ability to provide quick and compassionate service will enhance its brand value and go a long way in building a loyal customer base.

The obvious question, which then arises is, “How can an organization respond to these challenges?” Let’s find out.

The To-Do List: Addressing Key Challenges

Before delving into the potential solutions, it is imperative to understand the key areas of concern that require swift action by the organizations. These include –

  • Enhancing the quality of service by reducing customer wait-times
  • Leveraging automation to control the average wait-time
  • Adopting digital and self-service channels to address repetitive issues
  • Ensuring the availability of consistent and accurate information to customers
  • Ensuring scalability to meet the increased demand for customer support
  • Facilitating personalized customer experiences 24×7

Building A Resilient Customer Service

To navigate through the crisis, organizations must modernize their existing IT infrastructure. As a part of this modernization process, our customers and several large enterprises are adopting a myriad of advanced technologies. But the most popular of them are conversational AI and data analytics. 

The combination of these technologies is enabling companies to effectively manage the growing demand for customer support, while reducing the burden on the support staff. 

While analytics provide insights on the changing customer behaviour, emerging customer issues and common roadblocks in customer service operations, conversational AI can be used to deliver the first line of service and improve agent productivity.

1. Use Data Analytics To Enable A Forward-Thinking Approach

Utilizing data to the fullest has never been more important. In the midst of this volatility, contact centers must utilize data to

  • Predict customer demand
  • Predict workforce supply

         This can be done in the following manner –

  • Predict Customer Demand: Forecasting models used prior to the pandemic are no longer viable to use. To predict the volume of calls in the new normal, data related a region’s economic activity and population health must be analyzed. Additionally, companies must also analyze the data pertaining to customer demographics, impact due to company actions, the volume of COVID-19 related calls in other virus-affected areas, PHI data, social media sentiment data, data derived from agents, etc. By analyzing this data with predictive and prescriptive analytics, companies can be better prepared to manage the influx of call volumes.
  • Predict Workforce Supply: A similar approach can be taken to predict any deviations in the planned workflow supply. Factors like workflow demographics, the spread of the virus in areas where members of the staff reside, etc. must be considered to predict the availability of contact center staff.

In addition to these two approaches, analytics can also be used to identify and deflect non-critical calls, requests and contacts to virtual agents or other self-service channels.

2. Use Conversational AI To Deliver Service At Scale

Conversational AI is an advanced technology that enables computers to process, understand and respond to text or voice inputs in natural language. Conversational AI platforms can be used to build conversational user interfaces, chatbots and virtual assistants which can be integrated with messaging platforms, social media platforms, SMS, and website chat.

Conversational AI technologies like chatbots and conversational IVR are designed to understand and provide answers to customer queries with minimal agent intervention. They can act as first line of support and improve the productivity of human support staff in the following ways –

  • Using data analysis, organizations can identify the primary reasons customers are reaching out to the support staff and can employ a dedicated chatbot on the website or mobile app to answer the repeat queries. This will allow the service agents to focus on the more critical and complex customer needs;
  • Conversational IVR systems which are enabled by Natural Language Processing (NLP) can handle the sudden surge in calls and reduce panic among the customers;
  • Chatbots can also monitor conversations and inform organizations about the frequently asked customer queries;
  • Bots can draw up the relevant knowledge and background information from CRM systems to assist contact center agents in servicing customers quickly. This will improve first call resolution rates
  • They can transfer calls or conversations to the right agents in case of more complex queries that require human attention

Learn More: Human Handoff In Service Desk Bots

To realize the potential of chatbots completely amid this crisis, organizations must be mindful of a few things –

  • Time is of the essence when implementing conversational AI solutions. It is prudent for the organizations to quickly set-up and adopt the technology to keep up with the rapidly evolving needs of the customers. Hence, there is a need for low-code bot-builder platforms like Acuvate’s BotCore or Microsoft’s Power Virtual Agents for quick deployment of virtual agents.
  • While designing chatbots, it is crucial to be sensitive to the customers’ emotions and feelings. For this it is necessary to use empathetic language and be proactive in answering the customers’ queries.Using sentiment analysis in chatbots can enable them to understand human emotions.
  • Offering personalized solutions is the key to gaining the customer’s faith. The chatbot must integrate with third-party databases to draw up relevant background knowledge. This helps in providing personalized solutions that are specific to the needs of the customer. This reassures the customer that he is understood and valued by the organization;
  • Consistency of information is the key to superior customer service. The information provided by chatbots must align with the information given by call center agents to avoid any confusion or misunderstanding.
  • The chatbot must stay up-to-date on the needs of the customers. Drawing up the old chatbot conversation data helps the organizations in understanding the frequently asked topics. They can then stay relevant by designing new chatbot conversations and workflows to address these issues.

Learn More:

There are several real-life scenarios where companies have adopted chatbots amid COVID-19 to keep up with the growing customer needs –

  • Bank of America introduced its chatbot ‘Erica’ inside its mobile banking app. From providing balance information, tracking spending trends to suggesting ways to save money to providing personalized recommendations, Erica has been helping customers address their banking problems. Erica added 1 million users a month from March through May 2020.

Since March, it has assisted 350,000 clients who had trouble meeting their credit card, auto loan and other payment obligations, thereby reducing the call volume to the bank’s contact centres.

  • Reliance General Insurance – A large insurance company based in India, Reliance General Insurance deployed an NLP based chatbot called RIVA, which is available on the company website, WhatsApp and Facebook Messenger. RIVA can generate a policy quotation, accept claim intimation, and provide a soft copy of the claim and policy in less than a minute.

The bot has handled more than 20,000 transactions handled monthly and 70% of chats handled have an average handling time of less than 2 minutes. The bot reduced the company’s operational cost by 60%.

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We, at Acuvate Software, are helping clients to streamline their customer service operations and reduce costs through our chatbot-builder platform called BotCore.

BotCore presents several benefits – (i) it is an intuitive, no-code platform to easily build AI-powered bots, (ii) it is versatile and can seamlessly integrate with existing legacy systems and AI services, (iii) it allows the connection of existing bots across different departments to create a strong digital network, (iv) it is deployable on both on-site and cloud environments, (v) it facilitates the creation of chatbots that can simulate highly complex conversations.

Get Started

The pandemic has accelerated digital transformation initiatives in different functions of an organization and customer service is no different. To build a resilient customer service and infuse agility in day-to-day operations, organizations must continually evolve and adapt to the changing times. They must revamp their existing IT infrastructures to manage the pandemic-driven needs of the customers.

This response to the crisis is likely to transform the future of customer service forever. Adopting conversational AI and data analytics is a good starting point for organizations looking to transform their operations.

Since it is hard to predict the end of the crisis, organizations must plan both for short-term continuity as well as enabling long-term operational changes.

If you’d like to learn more about this topic, please feel free to get in touch with one of our chatbot experts for a personalized consultation.

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Take Your Chatbots To The Next Level With These New Capabilities https://botcore.ai/blog/take-your-chatbots-to-the-next-level-with-these-new-capabilities/ Fri, 29 Mar 2019 13:25:36 +0000 https://botcore.ai/?p=4847 Take Your Chatbots To The Next Level With These New Capabilities The adoption of chatbots in enterprises has grown exponentially in the last decade and today, we can see organizations of all sizes using bots for a variety of use cases and functions. A report by Markets and Markets shows that the chatbot market will […]

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Take Your Chatbots To The Next Level With These New Capabilities

The adoption of chatbots in enterprises has grown exponentially in the last decade and today, we can see organizations of all sizes using bots for a variety of use cases and functions. A report by Markets and Markets shows that the chatbot market will be worth $9.4 billion by 2024. 

The chatbot technology has evolved greatly in the past decade. Bots today are equipped with a host of new capabilities and have become more sophisticated. In order to gain greater business value from bots and use them in multi-faceted ways, it’s imperative for organizations to upgrade their bots. Without further ado, here are some significant and modern capabilities you should equip your enterprise chatbot with.

Explore these New Chatbot Capabilities

1. DataOps with chatbots

A large amount of data is captured from conversational technologies like chatbots, which are a crucial channel for gathering data. Data analytics employs new approaches like DataOps to leverage data that is captured through chatbots. This data can be analyzed and integrated with the other sources of internal and external data for better marketing and customer service.

2. Chatbot – RPA Integration

Integrating chatbots (front-office bots) with Robotic Process Automation (RPA) can enable them to navigate through back-end enterprise systems that don’t have modern APIs. RPA enables chatbots to retrieve information from these systems and handle more complex and real-time customer/employee requests and queries at scale. RPA bots can perform more mundane tasks without routing them to a human agent.

By combining the power of automation from RPA and cognitive intelligence of chatbots, organizations can take their customer and employee experience to the next level, improve productivity, reduce costs and increase competitive advantage.

3. Make chatbots repeatable

Companies deploying chatbots for their business processes need to standardize chatbots and make them consistent across all channels – for instance, chatbots deployed on a company’s social media pages cannot be different from the one deployed on their official page.

4. Voice Bots

The next generation of AI assistants in the enterprise is the voice-based virtual assistants. Enabling chatbots to interact through voice relieves the agents and customers from the need to use devices (like mouse and keyboard) to interact with business applications.

According to Gartner, By 2023, 25% of employee interactions with applications will happen via voice, up from almost 3% in 2019. Voice bots can deliver more personalized responses with contextual understanding, speech synthesis, voice recognition, and natural language processing.

Read More: How Voice Assistants are transforming the enterprise workplace

5. Incorporation Of Payments Within Chatbots

According to reports, 67% of US millennials said they are likely to purchase products and services from brands using a chatbot  Incorporating automated payments in the process paves the way to easier purchasing during online shopping as it eliminates the need to visit a website, as chatbots will be able to handle the entire purchasing process. 

6. Sentiment Analysis

Sentiment analysis empowers chatbots with the ability to understand the emotions and mood of the user by analyzing their text or voice input. This helps chatbots to drive the conversation wisely and deliver appropriate responses. The purpose of sentiment analysis is to provide a personalized experience and make chatbots effectively respond according to the customer’s mood.

Using this technology you can understand customers’ perception of the brand, enable seamless agent handoff, improve upselling/cross-selling and deliver memorable customer experiences.

7. Improve the ability to conduct complex conversations

A key driver of user adoption is the chatbot’s ability to understand complex responses that could have multiple intents. Chatbots should predict not only what customers want but also any key information they might have forgotten.

This could be achieved by upgrading your dialog systems with new features like knowledge graph, contextual understanding, topic switching, sentiment analysis, personality, etc. 

8. Handling escalations using chatbots

Intelligent digital assistants can be trained to easily escalate a customer issue to a human agent using rules or failure conditions. Other options include a built-in live chat support app for your agents or you can integrate your existing live chat software as well. 

Chatbot technology is becoming increasingly sophisticated and organizations should keep up with it in order to ensure improved user experience.  In 2020 and beyond we can see more intelligent chatbots that can integrate with disparate systems, understand intent better, conduct more complex conversations and deliver meaningful responses. 

If you’d like to learn more about this topic, please feel free to get in touch with one of our enterprise chatbot consultants for a personalized consultation.

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Understanding The Role Of Sentiment Analysis In Chatbots https://botcore.ai/blog/understanding-the-role-of-sentiment-analysis-in-chatbots/ Fri, 01 Mar 2019 10:00:00 +0000 https://botcore.ai/?p=4003 Understanding The Role Of Sentiment Analysis In Chatbots Chatbot technology has had an undeniable impact on digital transformation of organizations; customer experience management, in particular. When we talk about conversational AI solutions and other AI-based applications for augmenting customer  and employee experience, chatbots emerge as a front-runner. According to Gartner, 70% of white-collar workers will engage […]

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Understanding The Role Of Sentiment Analysis In Chatbots

Chatbot technology has had an undeniable impact on digital transformation of organizations; customer experience management, in particular. When we talk about conversational AI solutions and other AI-based applications for augmenting customer  and employee experience, chatbots emerge as a front-runner.

According to Gartner, 70% of white-collar workers will engage with conversational platforms on a regular basis in the next three years. The research firm’s 2019 CIO Survey revealed that chatbots are the main AI-based application used by the participating companies. Therefore, we can see increased investment in chatbot development and deployment.

“There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years. This increase has been driven by customer service, knowledge management and user support,” shared Van Baker, VP Analyst at Gartner.

However, in the earlier stages, chatbots used to have limited capabilities and deliver standard responses. With the advancements in AI and machine learning, chatbots have become more powerful and incorporated new features that helped improve user experience. And one of these latest features that is taking user experience to the next level is sentiment analysis.

Sentiment analysis helps a chatbot to understand the emotions and state of mind of the users by analyzing their input text or voice. This analysis enables chatbots to better steer conversations and deliver the right responses. Sentiment analysis is also playing a key role in driving user adoption for enterprise chatbots.

Let’s deep dive!

understanding sentiment analysis

Sentiment analysis is a sub field of machine learning and natural language processing that deals with extracting thoughts, opinions, or sentiments from voice or textual data. It is currently widely used in marketing and customer service functions to analyse customer data from surveys, social media and reviews. This not only enables businesses to understand the impact of their products/services but also  to tweak their strategies as per the end consumers’ opinions.

In the context of chatbots, sentiment analysis helps in developing the bot’s emotional intelligence.

While machine learning helps to personalize the chatbot’s performance by harnessing historical customer data, NLP helps to evaluate and interpret the information sent by the customer in real-time.

These two features collectively help chatbots to deliver relevant responses and conduct meaningful conversations. Sentiment analysis takes this a step further by enabling bots to understand human moods and emotions.

Let’s break down how sentiment analysis in chatbots works:

  • It first identifies sentiment types and gauges if the emotions displayed in the conversation are positive, negative, neutral or objective. The technology detects emotions like anger, happiness, disgust, fear, sadness, curiosity, positivity and other range of emotions.

  • NLP and AI work in tandem to measures the intensity of the emotions and assign a numerical score to each of the core emotions.

  • After detection and classification, sentiment analysis presents the final output that enables chatbot to steer the conversation in the right direction. For example, for a text with a high positive score (joy + happiness), the digital assistant can use that as an opportunity for product recommendation or sales conversion. And in the case of a high negative score (sad + anger), the chatbot can escalate the complaint and transfer the call to a live support agent.

how can it be beneficial for your business?

Be it banking, insurance, hospitality, healthcare, travel or eCommerce, all customer-facing industries can benefit from sophisticated new-age chatbots that are integrated with sentiment analysis.

Take, American cosmetics brand CoverGirl, for instance. The company developed an influencer chatbot enabled by sentiment analysis, which helped them to improve mobile commerce performance. 91%  of the conversations via the chatbot earned positive sentiment, and on an average 17 messages were exchanged per conversation that reflects high engagement rate. In addition, 48% of those conversations led to coupon delivery and the coupons’ click through-rate was an impressive 51%.

The above example illustrates the effectiveness of sentiment analysis-powered chatbots in stimulating conversations, identifying customers’ intentions, providing relevant answers and delivering a meaningful customer experience.

Listed below are a few of the benefits of using a sentiment analysis enabled chatbot to augment customer experience.

  • Learn how customers feel about your brand

Emotions heavily influence a person’s decision making process. How a customer is feeling determines the length and the nature of the relationship with the brand. Make use of this technology to understand how customers are feeling about your brand and communicate effectively at any stage of the customer lifecycle.

  • Seamless agent handover

It is important to understand the impact of timely escalation of issues to human agents when it comes to customer service chatbots. In the absence of sentiment analysis, chatbots would not be able to sense the tone of the aggrieved customer. But a digital assistant with emotional intelligence will help businesses to deal with displeased customers in an efficient manner. If the customer sounds frustrated or angry, the bot can easily hand off the conversation to a human agent.

Learn more: Human Hand-off in Service Desk Bots

  • Memorable customer experiences

The basic intent of sentiment analysis is to personalize and modify a chatbot’s responses to match the customer’s mood. This will enable businesses to build engaging conversations with customers at a very early stage and create a delightful customer experience.

  • Keep track of performance

The biggest benefit of using sentiment analysis is that it provides unique and powerful consumer insights. The conversations of an emotionally intelligent chatbot can act as a treasure trove of accurate data, which can be used to measure effectiveness of products/service, design future strategies, segment the customer base and devise strong brand positioning.

  • Upselling and new user on-boarding

As exemplified by CoverGirl’s influencer bot, chatbots can assist companies in product discovery, recommendation and upselling their products & services to existing customers. It can also improve new customer acquisition metrics by retaining the interest of a new visitor by analyzing his/her sentiments.

As stated above, emotions influence decision-making. Sentiment analysis help chatbots to adapt to the users’ mood and respond accurately, effectively and in the right way. By deploying and investing in this technology, companies can not only improve customer experience but also allow human agents to focus on productive issues.

If you’d like to learn more about the role of sentiment analysis in chatbots, please feel free to get in touch with one of our AI chatbot consultants for a personalized consultation.

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